UBER Analytics

library(ggplot2)
library(ggthemes)
library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyr)
library(DT)
library(scales)
library(tidyverse)

## -- Attaching packages ----------------------------------- tidyverse 1.3.0 --

## v tibble  3.0.3     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.5.0
## v purrr   0.3.4

## -- Conflicts -------------------------------------- tidyverse_conflicts() --
## x lubridate::as.difftime() masks base::as.difftime()
## x readr::col_factor()      masks scales::col_factor()
## x lubridate::date()        masks base::date()
## x purrr::discard()         masks scales::discard()
## x dplyr::filter()          masks stats::filter()
## x lubridate::intersect()   masks base::intersect()
## x dplyr::lag()             masks stats::lag()
## x lubridate::setdiff()     masks base::setdiff()
## x lubridate::union()       masks base::union()

The first step is to important all of the data.

Then because we have very messy and unformatted data - particularly the dates and times - we need to clean this up such that we can process the data correctly. Therefore, we use lubridate to convert out data into an understandable format.

## # A tibble: 4,534,327 x 9
##    Date_Time        Date       Year  Month Day   DayOfWeek   Lat   Lon Base  
##    <chr>            <fct>      <fct> <fct> <fct> <ord>     <dbl> <dbl> <chr> 
##  1 04/01/2014 00:11 2014-04-01 2014  April 01    Tue        40.8 -74.0 B02512
##  2 04/01/2014 00:17 2014-04-01 2014  April 01    Tue        40.7 -74.0 B02512
##  3 04/01/2014 00:21 2014-04-01 2014  April 01    Tue        40.7 -74.0 B02512
##  4 04/01/2014 00:28 2014-04-01 2014  April 01    Tue        40.8 -74.0 B02512
##  5 04/01/2014 00:33 2014-04-01 2014  April 01    Tue        40.8 -74.0 B02512
##  6 04/01/2014 00:33 2014-04-01 2014  April 01    Tue        40.7 -74.0 B02512
##  7 04/01/2014 00:39 2014-04-01 2014  April 01    Tue        40.7 -74.0 B02512
##  8 04/01/2014 00:45 2014-04-01 2014  April 01    Tue        40.8 -74.0 B02512
##  9 04/01/2014 00:55 2014-04-01 2014  April 01    Tue        40.8 -74.0 B02512
## 10 04/01/2014 01:01 2014-04-01 2014  April 01    Tue        40.8 -74.0 B02512
## # ... with 4,534,317 more rows

Now we want to see how the number of trips made by Uber varies throughout the week. Therefore, we find make a wonderful data displaying this.

Unfortunately, number arent as easy to digest, so lets plot this on a graph.

Alright, but its still kind of hard to understand when its busiest for Uber, so lets try this out…

## `summarise()` regrouping output by 'DayOfWeek' (override with `.groups` argument)

library(leaflet)
library(leaflet.extras)

leaflet(data = apr_data) %>% 
  
  addProviderTiles("OpenStreetMap.Mapnik") %>% 
  
  addCircleMarkers(lng = ~Lon, 
                   lat = ~Lat,
                   clusterOptions = markerClusterOptions()
                   ) %>% 
  addHeatmap(lng = ~Lon, 
             lat = ~Lat,
             radius = 15,
             blur = 40,
             cellSize = 30
             )